plot.dbplsr {dbstats}

Plots for a dbplsr object

Description

Four plots (selected by which) are available: plot of scores, response vs scores, R2 contribution in each component and the value of "OCV", "GCV", "AIC" or "BIC" vs the number of component chosen.

Usage

## S3 method for class 'dbplsr'
plot(x,which=c(1L:4L),main="",scores.comps=1:2,
        component=1,method=c("OCV","GCV","AIC","BIC"),...)

Arguments

x

an object of class dbplsr.

which

if a subset of the plots is required, specify a subset of the numbers 1:4.

main

an overall title for the plot. Only if one of the four plots is selected.

scores.comps

array containing the component scores crossed in the first plot (default the first two).

component

numeric value. Component vs response in the second plot (Default the first component).

method

choosen method "OCV", "GCV", "AIC" or "BIC" in the last plot.

...

other parameters to be passed through to plotting functions.

Author(s)

Boj, Eva <[e-mail]>, Caballe, Adria <[e-mail]>, Delicado, Pedro <[e-mail]> and Fortiana, Josep <[e-mail]>

References

Boj E, Delicado P, Fortiana J (2010). Distance-based local linear regression for functional predictors. Computational Statistics and Data Analysis 54, 429-437.

Boj E, Grane A, Fortiana J, Claramunt MM (2007). Implementing PLS for distance-based regression: computational issues. Computational Statistics 22, 237-248.

Boj E, Grane A, Fortiana J, Claramunt MM (2007). Selection of predictors in distance-based regression. Communications in Statistics B - Simulation and Computation 36, 87-98.

Cuadras CM, Arenas C, Fortiana J (1996). Some computational aspects of a distance-based model for prediction. Communications in Statistics B - Simulation and Computation 25, 593-609.

Cuadras C, Arenas C (1990). A distance-based regression model for prediction with mixed data. Communications in Statistics A - Theory and Methods 19, 2261-2279.

Cuadras CM (1989). Distance analysis in discrimination and classification using both continuous and categorical variables. In: Y. Dodge (ed.), Statistical Data Analysis and Inference. Amsterdam, The Netherlands: North-Holland Publishing Co., pp. 459-473.

Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

See Also

dbplsr for distance-based partial least squares.

Examples

#require(pls)
library(pls)
data(yarn)
## Default methods:
yarn.dbplsr <- dbplsr(density ~ NIR, data = yarn, ncomp=6, method="GCV")
plot(yarn.dbplsr,scores.comps=1:3)